Spiking neural networks implemented in dynamic neuromorphic processors are well suited for spatiotemporal feature detection and learning, for example in ultra low-power embedded intelligence and deep edge applications. Such pattern recognition networks naturally involve a combination of dynamic delay mechanisms and coincidence detection. Inspired by an auditory feature detection circuit in crickets, featuring a delayed excitation by postinhibitory rebound, we investigate disynaptic delay elements formed by inhibitory-excitatory pairs of dynamic synapses. We configure such disynaptic delay elements in the DYNAP-SE neuromorphic processor and characterize the distribution of delayed excitations resulting from device mismatch. Furthermore, we present a network that mimics the auditory feature detection circuit of crickets and demonstrate how varying synapse weights, input noise and processor temperature affects the circuit. Interestingly, we find that the disynaptic delay elements can be configured such that the timing and magnitude of the delayed postsynaptic excitation depend mainly on the efficacy of the inhibitory and excitatory synapses, respectively. Delay elements of this kind can be implemented in other reconfigurable dynamic neuromorphic processors and opens up for synapse level temporal feature tuning with large fan-in and flexible delays of order 10-100 ms.

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Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired “neuromorphic” processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital–computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

“Are electric cooking appliances viable clean cooking solutions for mini-grids?” To help answer this question, the Access to Energy Institute (A2EI) set up a pilot project in six different mini-grid locations around Lake Victoria in Tanzania and gave 100 households an electric pressure cooker (EPC) to use in their homes. Each EPC was connected to a smart meter to collect data on how the EPCs were used. The paper presents findings from a study designed around the A2EI pilot project that aims to provide an understanding of cooking practices, the adoption of electric cooking over time, and to assess the potential for electric cooking to substitute traditional cooking fuels. Through collaboration with the Modern Energy Cooking Services (MECS) program, Nexleaf Analytics, and PowerGen, the pilot has generated data on electrical energy consumption from 92 households in six remote areas as well as a comprehensive range of other datasets gathered from 28 households in two of the locations. This paper presents a preliminary analysis of this data. It starts with an analysis of cooking practices in these communities—dishes cooked, utensils used for cooking, and choice of fuels. It goes on to examine fuel stacking behavior, and finally, it examines how people have integrated EPCs into their cooking practices before the highlighting key impacts associated with using EPCs. The answer to the original research question will be useful for different stakeholders such as utility companies, mini-grid operators, electric cooking appliance manufacturers, the clean cooking sector, and international organizations.